We describe a method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell-type- and/or tissue-specific way (FUN-LDA). Using this ...unsupervised approach, we predict tissue-specific functional effects for every position in the human genome in 127 different tissues and cell types. We demonstrate the usefulness of our predictions by using several validation experiments. Using eQTL data from several sources, including the GTEx project, Geuvadis project, and TwinsUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap. We further show how these integrated functional scores can be used for (1) deriving the most likely cell or tissue type causally implicated for a complex trait by using summary statistics from genome-wide association studies and (2) estimating a tissue-based correlation matrix of various complex traits. We found large enrichment of heritability in functional components of relevant tissues for various complex traits, and FUN-LDA yielded higher enrichment estimates than existing methods. Finally, using experimentally validated functional variants from the literature and variants possibly implicated in disease by previous studies, we rigorously compare FUN-LDA with state-of-the-art functional annotation methods and show that FUN-LDA has better prediction accuracy and higher resolution than these methods. In particular, our results suggest that tissue- and cell-type-specific functional prediction methods tend to have substantially better prediction accuracy than organism-level prediction methods. Scores for each position in the human genome and for each ENCODE and Roadmap tissue are available online (see Web Resources).
Recent developments of high‐throughput genomic technologies offer an unprecedented detailed view of the genetic variation in various human populations, and promise to lead to significant progress in ...understanding the genetic basis of complex diseases. Despite this tremendous advance in data generation, it remains very challenging to analyze and interpret these data due to their sparse and high‐dimensional nature. Here, we propose novel applications and new developments of empirical Bayes scan statistics to identify genomic regions significantly enriched with disease risk variants. We show that the proposed empirical Bayes methodology can be substantially more powerful than existing scan statistics methods especially so in the presence of many non‐disease risk variants, and in situations when there is a mixture of risk and protective variants. Furthermore, the empirical Bayes approach has greater flexibility to accommodate covariates such as functional prediction scores and additional biomarkers. As proof‐of‐concept we apply the proposed methods to a whole‐exome sequencing study for autism spectrum disorders and identify several promising candidate genes.
We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin ...interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at Formula: see text of the associated loci.
Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be ...very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Introduction
We investigated metabolites in plasma to capture systemic biochemical changes associated with Alzheimer's disease (AD).
Methods
Metabolites in plasma were measured in 59 AD cases and 60 ...healthy participants of African American (AA), Caribbean Hispanic (CH), and non‐Hispanic white (NHW) ancestry using untargeted liquid‐chromatography–based ultra‐high‐resolution mass spectrometry. Metabolite differences between AD and healthy, ethnic groups and apolipoprotein E gene (APOE) ε4 status were analyzed. Untargeted network analysis identified pathways enriched in AD‐associated metabolites.
Results
A total of 5929 annotated metabolites were measured. Partial least squares discriminant analysis (PLS‐DA) inferred that AD clustered separately from healthy controls (area under the curve AUC = 0.9816); discriminating pathways included glycerophospholipid, sphingolipid, and non‐essential amino acid (alanine, aspartate, glutamate) metabolism. Metabolic features in AA clustered differently from CH and NHW (AUC = 0.9275), and differed between APOE ε4 carriers and non‐carriers (AUC = 0.9972).
Discussion
Metabolites, specifically lipids, were associated with AD, APOE ε4, and ethnic group. Metabolite profiling can identify perturbed AD pathways, but genetic and ancestral background need to be considered.
Rapid advances in sequencing technologies set the stage for the large-scale medical sequencing efforts to be performed in the near future, with the goal of assessing the importance of rare variants ...in complex diseases. The discovery of new disease susceptibility genes requires powerful statistical methods for rare variant analysis. The low frequency and the expected large number of such variants pose great difficulties for the analysis of these data. We propose here a robust and powerful testing strategy to study the role rare variants may play in affecting susceptibility to complex traits. The strategy is based on assessing whether rare variants in a genetic region collectively occur at significantly higher frequencies in cases compared with controls (or vice versa). A main feature of the proposed methodology is that, although it is an overall test assessing a possibly large number of rare variants simultaneously, the disease variants can be both protective and risk variants, with moderate decreases in statistical power when both types of variants are present. Using simulations, we show that this approach can be powerful under complex and general disease models, as well as in larger genetic regions where the proportion of disease susceptibility variants may be small. Comparisons with previously published tests on simulated data show that the proposed approach can have better power than the existing methods. An application to a recently published study on Type-1 Diabetes finds rare variants in gene IFIH1 to be protective against Type-1 Diabetes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture ...a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.
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•Emergency contact information used to identify 7.4 million familial relationships.•Familial relationships were validated using clinical and genetic data•Estimated heritability for 500 traits using only medical records data•Heritability estimates were concordant across study sites and with the literature
Electronic health records can be mined for familial relationships that provide distinct insights into heritability of human disease.
Common genetic variants confer susceptibility to a large number of complex brain disorders. Given that such variants predominantly localize in non-coding regions of the human genome, there is a ...significant challenge to predict and characterize their functional consequences. More importantly, most available computational methods, generally defined as context-free methods, output prediction scores regarding the functionality of genetic variants irrespective of the context, i.e., the tissue or cell-type affected by a disease, limiting the ability to predict the functional consequences of common variants on brain disorders. In this study, we introduce a comparative multi-step pipeline to investigate the relative effectiveness of context-specific and context-free approaches to prioritize disease causal variants. As an experimental case, we focused on schizophrenia (SCZ), a debilitating neuropsychiatric disease for which a large number of susceptibility variants is identified from genome-wide association studies. We tested over two dozen available methods and examined potential associations between the cell/tissue-specific mapping scores and open chromatin accessibility, and provided a prioritized map of SCZ risk loci for
or
functional analysis. We found extensive differences between context-free and tissue-specific approaches and showed how they may play complementary roles. As a proof of concept, we found a few sets of genes, through a consensus mapping of both categories, including
to be among the top hits. We showed that the genetic variants in this gene and related genes collectively dysregulate gene expression patterns in stem cell-derived neurons and characterize SCZ phenotypic manifestations, while genes which were not shared among highly prioritized candidates in both approaches did not demonstrate such characteristics. In conclusion, by combining context-free and tissue-specific predictions, our pipeline enables prioritization of the most likely disease-causal common variants in complex brain disorders.
Aberrant O-glycosylation of serum immunoglobulin A1 (IgA1) represents a heritable pathogenic defect in IgA nephropathy, the most common form of glomerulonephritis worldwide, but specific genetic ...factors involved in its determination are not known. We performed a quantitative GWAS for serum levels of galactose-deficient IgA1 (Gd-IgA1) in 2,633 subjects of European and East Asian ancestry and discovered two genome-wide significant loci, in C1GALT1 (rs13226913, P = 3.2 x 10-11) and C1GALT1C1 (rs5910940, P = 2.7 x 10-8). These genes encode molecular partners essential for enzymatic O-glycosylation of IgA1. We demonstrated that these two loci explain approximately 7% of variability in circulating Gd-IgA1 in Europeans, but only 2% in East Asians. Notably, the Gd-IgA1-increasing allele of rs13226913 is common in Europeans, but rare in East Asians. Moreover, rs13226913 represents a strong cis-eQTL for C1GALT1 that encodes the key enzyme responsible for the transfer of galactose to O-linked glycans on IgA1. By in vitro siRNA knock-down studies, we confirmed that mRNA levels of both C1GALT1 and C1GALT1C1 determine the rate of secretion of Gd-IgA1 in IgA1-producing cells. Our findings provide novel insights into the genetic regulation of O-glycosylation and are relevant not only to IgA nephropathy, but also to other complex traits associated with O-glycosylation defects, including inflammatory bowel disease, hematologic disease, and cancer.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ...ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) a meta-analysis for Alzheimer's disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.
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